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🎲SimulationsπŸ§ͺLab≣Catalog?How this works

sweep: match.red_card_shown (bool)

← lab Β· AUC 0.609 (real signal) Β· ran 7/5/2026

What this is: Asks which pre-match factors drive one specific outcome, using a walk-forward model and permutation importance.
FactorImportanceDirectionSurvives all eras?
home__t3__team.elo0.0143↓ -0.185no
away__team.congestion_21d0.0000↑ +0.032no
away__team.elo_momentum_l50.0000β€”no
away__team.elo0.0000↓ -0.061no
away__team.goal_diff_avg_l50.0000β€”no
away__team.goals_against_avg_l50.0000β€”no
away__team.goals_for_avg_l50.0000β€”no
away__team.form_points_l50.0000β€”no
away__team.matches_since_clean_sheet0.0000↓ -0.093no
away__team.matches_since_win0.0000↓ -0.041no
away__team.pass_acc_avg_l50.0000↑ +0.073no
away__team.possession_avg_l50.0000↑ +0.050no

Reading the columnswhat each number actually means

AUCpredictability: 0.50 = coin flip, ~0.70 = ceiling for sports
Importancehow much the model leans on this factor (permutation importance)
Directionsign of the raw correlation with the outcome
Survives all eraseffect points the same way in every historical era
Spec Β· the reproducible recipe
{
  "name": "sweep: match.red_card_shown (bool)",
  "sport": "football",
  "target": {
    "metric": "match.red_card_shown"
  },
  "features": "all"
}